Overview

Brought to you by YData

Dataset statistics

Number of variables29
Number of observations4600
Missing cells122288
Missing cells (%)91.7%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory1.0 MiB
Average record size in memory232.0 B

Variable types

Text20
DateTime1
Numeric6
Unsupported1
Categorical1

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
All Time Rank is highly overall correlated with Track ScoreHigh correlation
Amazon Playlist Count is highly overall correlated with Apple Music Playlist Count and 1 other fieldsHigh correlation
Apple Music Playlist Count is highly overall correlated with Amazon Playlist Count and 1 other fieldsHigh correlation
Deezer Playlist Count is highly overall correlated with Amazon Playlist Count and 1 other fieldsHigh correlation
Track Score is highly overall correlated with All Time RankHigh correlation
Track has 4155 (90.3%) missing values Missing
Album Name has 4155 (90.3%) missing values Missing
Artist has 4156 (90.3%) missing values Missing
Release Date has 4155 (90.3%) missing values Missing
ISRC has 4155 (90.3%) missing values Missing
All Time Rank has 4155 (90.3%) missing values Missing
Track Score has 4155 (90.3%) missing values Missing
Spotify Streams has 4169 (90.6%) missing values Missing
Spotify Playlist Count has 4160 (90.4%) missing values Missing
Spotify Playlist Reach has 4160 (90.4%) missing values Missing
Spotify Popularity has 4221 (91.8%) missing values Missing
YouTube Views has 4187 (91.0%) missing values Missing
YouTube Likes has 4189 (91.1%) missing values Missing
TikTok Posts has 4248 (92.3%) missing values Missing
TikTok Likes has 4234 (92.0%) missing values Missing
TikTok Views has 4234 (92.0%) missing values Missing
YouTube Playlist Reach has 4226 (91.9%) missing values Missing
Apple Music Playlist Count has 4207 (91.5%) missing values Missing
AirPlay Spins has 4200 (91.3%) missing values Missing
SiriusXM Spins has 4274 (92.9%) missing values Missing
Deezer Playlist Count has 4220 (91.7%) missing values Missing
Deezer Playlist Reach has 4220 (91.7%) missing values Missing
Amazon Playlist Count has 4221 (91.8%) missing values Missing
Pandora Streams has 4229 (91.9%) missing values Missing
Pandora Track Stations has 4236 (92.1%) missing values Missing
Soundcloud Streams has 4401 (95.7%) missing values Missing
Shazam Counts has 4211 (91.5%) missing values Missing
TIDAL Popularity has 4600 (100.0%) missing values Missing
Explicit Track has 4155 (90.3%) missing values Missing
TIDAL Popularity is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2025-01-29 14:13:11.489503
Analysis finished2025-01-29 14:13:19.882129
Duration8.39 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Track
Text

Missing 

Distinct432
Distinct (%)97.1%
Missing4155
Missing (%)90.3%
Memory size36.1 KiB
2025-01-29T19:43:20.230377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length76
Median length48
Mean length16.244944
Min length3

Characters and Unicode

Total characters7229
Distinct characters82
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique423 ?
Unique (%)95.1%

Sample

1st rowMILLION DOLLAR BABY
2nd rowNot Like Us
3rd rowi like the way you kiss me
4th rowFlowers
5th rowHoudini
ValueCountFrequency (%)
45
 
3.5%
feat 36
 
2.8%
the 24
 
1.9%
me 16
 
1.2%
you 16
 
1.2%
i 14
 
1.1%
it 13
 
1.0%
with 13
 
1.0%
a 10
 
0.8%
like 10
 
0.8%
Other values (767) 1097
84.8%
2025-01-29T19:43:20.859548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
850
 
11.8%
e 552
 
7.6%
a 453
 
6.3%
o 396
 
5.5%
i 313
 
4.3%
n 289
 
4.0%
t 288
 
4.0%
r 262
 
3.6%
s 213
 
2.9%
l 180
 
2.5%
Other values (72) 3433
47.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4257
58.9%
Uppercase Letter 1515
 
21.0%
Space Separator 850
 
11.8%
Other Punctuation 234
 
3.2%
Other Number 109
 
1.5%
Open Punctuation 80
 
1.1%
Close Punctuation 77
 
1.1%
Decimal Number 69
 
1.0%
Dash Punctuation 37
 
0.5%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 552
13.0%
a 453
 
10.6%
o 396
 
9.3%
i 313
 
7.4%
n 289
 
6.8%
t 288
 
6.8%
r 262
 
6.2%
s 213
 
5.0%
l 180
 
4.2%
u 151
 
3.5%
Other values (17) 1160
27.2%
Uppercase Letter
ValueCountFrequency (%)
S 130
 
8.6%
T 116
 
7.7%
L 96
 
6.3%
A 95
 
6.3%
M 89
 
5.9%
B 83
 
5.5%
I 82
 
5.4%
D 74
 
4.9%
E 72
 
4.8%
C 70
 
4.6%
Other values (16) 608
40.1%
Other Punctuation
ValueCountFrequency (%)
¿ 109
46.6%
. 53
22.6%
' 21
 
9.0%
& 13
 
5.6%
" 12
 
5.1%
/ 6
 
2.6%
, 5
 
2.1%
: 5
 
2.1%
! 5
 
2.1%
? 4
 
1.7%
Decimal Number
ValueCountFrequency (%)
2 20
29.0%
0 15
21.7%
1 9
13.0%
3 8
 
11.6%
4 6
 
8.7%
5 5
 
7.2%
8 3
 
4.3%
9 1
 
1.4%
7 1
 
1.4%
6 1
 
1.4%
Open Punctuation
ValueCountFrequency (%)
( 77
96.2%
[ 3
 
3.8%
Close Punctuation
ValueCountFrequency (%)
) 74
96.1%
] 3
 
3.9%
Space Separator
ValueCountFrequency (%)
850
100.0%
Other Number
ValueCountFrequency (%)
½ 109
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 37
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5772
79.8%
Common 1457
 
20.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 552
 
9.6%
a 453
 
7.8%
o 396
 
6.9%
i 313
 
5.4%
n 289
 
5.0%
t 288
 
5.0%
r 262
 
4.5%
s 213
 
3.7%
l 180
 
3.1%
u 151
 
2.6%
Other values (43) 2675
46.3%
Common
ValueCountFrequency (%)
850
58.3%
½ 109
 
7.5%
¿ 109
 
7.5%
( 77
 
5.3%
) 74
 
5.1%
. 53
 
3.6%
- 37
 
2.5%
' 21
 
1.4%
2 20
 
1.4%
0 15
 
1.0%
Other values (19) 92
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6902
95.5%
None 327
 
4.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
850
 
12.3%
e 552
 
8.0%
a 453
 
6.6%
o 396
 
5.7%
i 313
 
4.5%
n 289
 
4.2%
t 288
 
4.2%
r 262
 
3.8%
s 213
 
3.1%
l 180
 
2.6%
Other values (69) 3106
45.0%
None
ValueCountFrequency (%)
ï 109
33.3%
½ 109
33.3%
¿ 109
33.3%

Album Name
Text

Missing 

Distinct406
Distinct (%)91.2%
Missing4155
Missing (%)90.3%
Memory size36.1 KiB
2025-01-29T19:43:21.377014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length163
Median length53
Mean length18.557303
Min length1

Characters and Unicode

Total characters8258
Distinct characters82
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique383 ?
Unique (%)86.1%

Sample

1st rowMillion Dollar Baby - Single
2nd rowNot Like Us
3rd rowI like the way you kiss me
4th rowFlowers - Single
5th rowHoudini
ValueCountFrequency (%)
61
 
4.4%
the 38
 
2.7%
single 35
 
2.5%
me 21
 
1.5%
feat 20
 
1.4%
you 14
 
1.0%
for 13
 
0.9%
and 11
 
0.8%
it 11
 
0.8%
a 11
 
0.8%
Other values (767) 1153
83.1%
2025-01-29T19:43:22.083910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
943
 
11.4%
e 566
 
6.9%
a 403
 
4.9%
o 375
 
4.5%
i 374
 
4.5%
n 335
 
4.1%
r 276
 
3.3%
t 272
 
3.3%
s 249
 
3.0%
½ 247
 
3.0%
Other values (72) 4218
51.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4456
54.0%
Uppercase Letter 1943
23.5%
Space Separator 943
 
11.4%
Other Punctuation 373
 
4.5%
Other Number 247
 
3.0%
Decimal Number 91
 
1.1%
Open Punctuation 78
 
0.9%
Close Punctuation 74
 
0.9%
Dash Punctuation 53
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 566
12.7%
a 403
 
9.0%
o 375
 
8.4%
i 374
 
8.4%
n 335
 
7.5%
r 276
 
6.2%
t 272
 
6.1%
s 249
 
5.6%
ï 247
 
5.5%
l 226
 
5.1%
Other values (17) 1133
25.4%
Uppercase Letter
ValueCountFrequency (%)
S 200
 
10.3%
T 163
 
8.4%
A 143
 
7.4%
E 138
 
7.1%
I 100
 
5.1%
O 100
 
5.1%
M 98
 
5.0%
D 95
 
4.9%
L 95
 
4.9%
R 87
 
4.5%
Other values (16) 724
37.3%
Other Punctuation
ValueCountFrequency (%)
¿ 247
66.2%
. 34
 
9.1%
' 26
 
7.0%
: 15
 
4.0%
" 12
 
3.2%
, 11
 
2.9%
& 10
 
2.7%
? 6
 
1.6%
/ 5
 
1.3%
! 4
 
1.1%
Other values (2) 3
 
0.8%
Decimal Number
ValueCountFrequency (%)
0 18
19.8%
1 16
17.6%
2 16
17.6%
3 12
13.2%
4 9
9.9%
5 6
 
6.6%
7 4
 
4.4%
8 4
 
4.4%
9 4
 
4.4%
6 2
 
2.2%
Open Punctuation
ValueCountFrequency (%)
( 74
94.9%
[ 4
 
5.1%
Close Punctuation
ValueCountFrequency (%)
) 70
94.6%
] 4
 
5.4%
Space Separator
ValueCountFrequency (%)
943
100.0%
Other Number
ValueCountFrequency (%)
½ 247
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 53
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6399
77.5%
Common 1859
 
22.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 566
 
8.8%
a 403
 
6.3%
o 375
 
5.9%
i 374
 
5.8%
n 335
 
5.2%
r 276
 
4.3%
t 272
 
4.3%
s 249
 
3.9%
ï 247
 
3.9%
l 226
 
3.5%
Other values (43) 3076
48.1%
Common
ValueCountFrequency (%)
943
50.7%
½ 247
 
13.3%
¿ 247
 
13.3%
( 74
 
4.0%
) 70
 
3.8%
- 53
 
2.9%
. 34
 
1.8%
' 26
 
1.4%
0 18
 
1.0%
1 16
 
0.9%
Other values (19) 131
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7517
91.0%
None 741
 
9.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
943
 
12.5%
e 566
 
7.5%
a 403
 
5.4%
o 375
 
5.0%
i 374
 
5.0%
n 335
 
4.5%
r 276
 
3.7%
t 272
 
3.6%
s 249
 
3.3%
l 226
 
3.0%
Other values (69) 3498
46.5%
None
ValueCountFrequency (%)
½ 247
33.3%
ï 247
33.3%
¿ 247
33.3%

Artist
Text

Missing 

Distinct275
Distinct (%)61.9%
Missing4156
Missing (%)90.3%
Memory size36.1 KiB
2025-01-29T19:43:22.739818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length92
Median length19
Mean length9.6981982
Min length2

Characters and Unicode

Total characters4306
Distinct characters68
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique200 ?
Unique (%)45.0%

Sample

1st rowTommy Richman
2nd rowKendrick Lamar
3rd rowArtemas
4th rowMiley Cyrus
5th rowEminem
ValueCountFrequency (%)
lil 12
 
1.6%
bad 11
 
1.5%
the 11
 
1.5%
bunny 10
 
1.3%
drake 10
 
1.3%
eilish 10
 
1.3%
billie 10
 
1.3%
g 8
 
1.1%
post 8
 
1.1%
malone 8
 
1.1%
Other values (411) 657
87.0%
2025-01-29T19:43:23.576096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 360
 
8.4%
311
 
7.2%
e 296
 
6.9%
i 259
 
6.0%
n 216
 
5.0%
r 200
 
4.6%
o 196
 
4.6%
l 165
 
3.8%
s 144
 
3.3%
u 107
 
2.5%
Other values (58) 2052
47.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2765
64.2%
Uppercase Letter 1072
 
24.9%
Space Separator 311
 
7.2%
Other Punctuation 64
 
1.5%
Other Number 63
 
1.5%
Decimal Number 26
 
0.6%
Dash Punctuation 2
 
< 0.1%
Currency Symbol 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 360
13.0%
e 296
10.7%
i 259
 
9.4%
n 216
 
7.8%
r 200
 
7.2%
o 196
 
7.1%
l 165
 
6.0%
s 144
 
5.2%
u 107
 
3.9%
t 102
 
3.7%
Other values (17) 720
26.0%
Uppercase Letter
ValueCountFrequency (%)
B 90
 
8.4%
S 78
 
7.3%
T 76
 
7.1%
L 74
 
6.9%
A 70
 
6.5%
M 64
 
6.0%
C 58
 
5.4%
D 53
 
4.9%
E 52
 
4.9%
P 51
 
4.8%
Other values (16) 406
37.9%
Decimal Number
ValueCountFrequency (%)
8 5
19.2%
5 5
19.2%
2 5
19.2%
4 5
19.2%
6 3
11.5%
1 2
 
7.7%
3 1
 
3.8%
Other Punctuation
ValueCountFrequency (%)
¿ 63
98.4%
: 1
 
1.6%
Space Separator
ValueCountFrequency (%)
311
100.0%
Other Number
ValueCountFrequency (%)
½ 63
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3837
89.1%
Common 469
 
10.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 360
 
9.4%
e 296
 
7.7%
i 259
 
6.8%
n 216
 
5.6%
r 200
 
5.2%
o 196
 
5.1%
l 165
 
4.3%
s 144
 
3.8%
u 107
 
2.8%
t 102
 
2.7%
Other values (43) 1792
46.7%
Common
ValueCountFrequency (%)
311
66.3%
¿ 63
 
13.4%
½ 63
 
13.4%
8 5
 
1.1%
5 5
 
1.1%
2 5
 
1.1%
4 5
 
1.1%
6 3
 
0.6%
- 2
 
0.4%
1 2
 
0.4%
Other values (5) 5
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4117
95.6%
None 189
 
4.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 360
 
8.7%
311
 
7.6%
e 296
 
7.2%
i 259
 
6.3%
n 216
 
5.2%
r 200
 
4.9%
o 196
 
4.8%
l 165
 
4.0%
s 144
 
3.5%
u 107
 
2.6%
Other values (55) 1863
45.3%
None
ValueCountFrequency (%)
¿ 63
33.3%
ï 63
33.3%
½ 63
33.3%

Release Date
Date

Missing 

Distinct321
Distinct (%)72.1%
Missing4155
Missing (%)90.3%
Memory size36.1 KiB
Minimum2011-11-20 00:00:00
Maximum2024-06-14 00:00:00
2025-01-29T19:43:23.735087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-29T19:43:23.971119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ISRC
Text

Missing 

Distinct445
Distinct (%)100.0%
Missing4155
Missing (%)90.3%
Memory size36.1 KiB
2025-01-29T19:43:24.376654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters5340
Distinct characters36
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique445 ?
Unique (%)100.0%

Sample

1st rowQM24S2402528
2nd rowUSUG12400910
3rd rowQZJ842400387
4th rowUSSM12209777
5th rowUSUG12403398
ValueCountFrequency (%)
ussm12200612 1
 
0.2%
usrc12300907 1
 
0.2%
usum72403305 1
 
0.2%
tcjpa2445163 1
 
0.2%
ussm12401302 1
 
0.2%
rua1h2415548 1
 
0.2%
bxweh2200054 1
 
0.2%
ussm12103949 1
 
0.2%
kre671700001 1
 
0.2%
usum72317276 1
 
0.2%
Other values (435) 435
97.8%
2025-01-29T19:43:24.907180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 699
13.1%
2 682
12.8%
1 534
 
10.0%
U 393
 
7.4%
S 329
 
6.2%
3 313
 
5.9%
4 303
 
5.7%
7 253
 
4.7%
9 218
 
4.1%
5 189
 
3.5%
Other values (26) 1427
26.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3557
66.6%
Uppercase Letter 1783
33.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 393
22.0%
S 329
18.5%
M 169
9.5%
A 109
 
6.1%
G 100
 
5.6%
Q 83
 
4.7%
B 74
 
4.2%
R 60
 
3.4%
C 49
 
2.7%
Z 48
 
2.7%
Other values (16) 369
20.7%
Decimal Number
ValueCountFrequency (%)
0 699
19.7%
2 682
19.2%
1 534
15.0%
3 313
8.8%
4 303
8.5%
7 253
 
7.1%
9 218
 
6.1%
5 189
 
5.3%
6 185
 
5.2%
8 181
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Common 3557
66.6%
Latin 1783
33.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 393
22.0%
S 329
18.5%
M 169
9.5%
A 109
 
6.1%
G 100
 
5.6%
Q 83
 
4.7%
B 74
 
4.2%
R 60
 
3.4%
C 49
 
2.7%
Z 48
 
2.7%
Other values (16) 369
20.7%
Common
ValueCountFrequency (%)
0 699
19.7%
2 682
19.2%
1 534
15.0%
3 313
8.8%
4 303
8.5%
7 253
 
7.1%
9 218
 
6.1%
5 189
 
5.3%
6 185
 
5.2%
8 181
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5340
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 699
13.1%
2 682
12.8%
1 534
 
10.0%
U 393
 
7.4%
S 329
 
6.2%
3 313
 
5.9%
4 303
 
5.7%
7 253
 
4.7%
9 218
 
4.1%
5 189
 
3.5%
Other values (26) 1427
26.7%

All Time Rank
Real number (ℝ)

High correlation  Missing 

Distinct444
Distinct (%)99.8%
Missing4155
Missing (%)90.3%
Infinite0
Infinite (%)0.0%
Mean222.82022
Minimum1
Maximum454
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.1 KiB
2025-01-29T19:43:25.082221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile23.2
Q1112
median223
Q3334
95-th percentile421.8
Maximum454
Range453
Interquartile range (IQR)222

Descriptive statistics

Standard deviation128.36494
Coefficient of variation (CV)0.57609196
Kurtosis-1.2003788
Mean222.82022
Median Absolute Deviation (MAD)111
Skewness-0.0033830512
Sum99155
Variance16477.558
MonotonicityNot monotonic
2025-01-29T19:43:25.279697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
355 2
 
< 0.1%
21 1
 
< 0.1%
22 1
 
< 0.1%
23 1
 
< 0.1%
24 1
 
< 0.1%
25 1
 
< 0.1%
26 1
 
< 0.1%
27 1
 
< 0.1%
28 1
 
< 0.1%
29 1
 
< 0.1%
Other values (434) 434
 
9.4%
(Missing) 4155
90.3%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
454 1
< 0.1%
443 1
< 0.1%
442 1
< 0.1%
441 1
< 0.1%
440 1
< 0.1%
439 1
< 0.1%
438 1
< 0.1%
437 1
< 0.1%
436 1
< 0.1%
435 1
< 0.1%

Track Score
Real number (ℝ)

High correlation  Missing 

Distinct360
Distinct (%)80.9%
Missing4155
Missing (%)90.3%
Infinite0
Infinite (%)0.0%
Mean128.84629
Minimum73.2
Maximum725.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.1 KiB
2025-01-29T19:43:25.469205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum73.2
5-th percentile75.1
Q184
median103.3
Q3144.5
95-th percentile278.98
Maximum725.4
Range652.2
Interquartile range (IQR)60.5

Descriptive statistics

Standard deviation74.359707
Coefficient of variation (CV)0.5771195
Kurtosis14.881547
Mean128.84629
Median Absolute Deviation (MAD)23.3
Skewness3.1629625
Sum57336.6
Variance5529.3661
MonotonicityDecreasing
2025-01-29T19:43:25.621205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79.9 4
 
0.1%
76.6 4
 
0.1%
76.2 4
 
0.1%
101.4 4
 
0.1%
79.5 3
 
0.1%
80.1 3
 
0.1%
75.4 3
 
0.1%
75.1 3
 
0.1%
75.3 3
 
0.1%
96.2 3
 
0.1%
Other values (350) 411
 
8.9%
(Missing) 4155
90.3%
ValueCountFrequency (%)
73.2 2
< 0.1%
73.4 2
< 0.1%
73.5 1
 
< 0.1%
73.6 2
< 0.1%
73.7 1
 
< 0.1%
73.9 1
 
< 0.1%
74 1
 
< 0.1%
74.1 2
< 0.1%
74.2 1
 
< 0.1%
74.4 3
0.1%
ValueCountFrequency (%)
725.4 1
< 0.1%
545.9 1
< 0.1%
538.4 1
< 0.1%
444.9 1
< 0.1%
423.3 1
< 0.1%
410.1 1
< 0.1%
407.2 1
< 0.1%
375.8 1
< 0.1%
355.7 1
< 0.1%
330.6 1
< 0.1%

Spotify Streams
Text

Missing 

Distinct424
Distinct (%)98.4%
Missing4169
Missing (%)90.6%
Memory size36.1 KiB
2025-01-29T19:43:26.354204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length11.352668
Min length5

Characters and Unicode

Total characters4893
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique418 ?
Unique (%)97.0%

Sample

1st row390,470,936
2nd row323,703,884
3rd row601,309,283
4th row2,031,280,633
5th row107,034,922
ValueCountFrequency (%)
1,655,575,417 3
 
0.7%
532,012,790 2
 
0.5%
311,928,522 2
 
0.5%
1,673,557,134 2
 
0.5%
250,080,413 2
 
0.5%
720,822,868 2
 
0.5%
121,234,000 1
 
0.2%
395,433,400 1
 
0.2%
670,009,914 1
 
0.2%
1,867,282 1
 
0.2%
Other values (414) 414
96.1%
2025-01-29T19:43:27.142827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 999
20.4%
1 504
10.3%
2 427
8.7%
3 381
 
7.8%
5 381
 
7.8%
7 376
 
7.7%
8 375
 
7.7%
9 372
 
7.6%
0 363
 
7.4%
4 359
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3894
79.6%
Other Punctuation 999
 
20.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 504
12.9%
2 427
11.0%
3 381
9.8%
5 381
9.8%
7 376
9.7%
8 375
9.6%
9 372
9.6%
0 363
9.3%
4 359
9.2%
6 356
9.1%
Other Punctuation
ValueCountFrequency (%)
, 999
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4893
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 999
20.4%
1 504
10.3%
2 427
8.7%
3 381
 
7.8%
5 381
 
7.8%
7 376
 
7.7%
8 375
 
7.7%
9 372
 
7.6%
0 363
 
7.4%
4 359
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4893
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 999
20.4%
1 504
10.3%
2 427
8.7%
3 381
 
7.8%
5 381
 
7.8%
7 376
 
7.7%
8 375
 
7.7%
9 372
 
7.6%
0 363
 
7.4%
4 359
 
7.3%
Distinct421
Distinct (%)95.7%
Missing4160
Missing (%)90.4%
Memory size36.1 KiB
2025-01-29T19:43:28.120824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.7818182
Min length1

Characters and Unicode

Total characters2544
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique414 ?
Unique (%)94.1%

Sample

1st row30,716
2nd row28,113
3rd row54,331
4th row269,802
5th row7,223
ValueCountFrequency (%)
2 6
 
1.4%
5 5
 
1.1%
1 5
 
1.1%
13 4
 
0.9%
23,181 2
 
0.5%
3 2
 
0.5%
217,189 2
 
0.5%
89,812 1
 
0.2%
358,760 1
 
0.2%
498 1
 
0.2%
Other values (411) 411
93.4%
2025-01-29T19:43:29.205932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 382
15.0%
1 322
12.7%
2 312
12.3%
3 230
9.0%
0 218
8.6%
8 192
7.5%
4 182
7.2%
7 181
7.1%
5 179
7.0%
9 177
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2162
85.0%
Other Punctuation 382
 
15.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 322
14.9%
2 312
14.4%
3 230
10.6%
0 218
10.1%
8 192
8.9%
4 182
8.4%
7 181
8.4%
5 179
8.3%
9 177
8.2%
6 169
7.8%
Other Punctuation
ValueCountFrequency (%)
, 382
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2544
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 382
15.0%
1 322
12.7%
2 312
12.3%
3 230
9.0%
0 218
8.6%
8 192
7.5%
4 182
7.2%
7 181
7.1%
5 179
7.0%
9 177
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2544
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 382
15.0%
1 322
12.7%
2 312
12.3%
3 230
9.0%
0 218
8.6%
8 192
7.5%
4 182
7.2%
7 181
7.1%
5 179
7.0%
9 177
7.0%
Distinct439
Distinct (%)99.8%
Missing4160
Missing (%)90.4%
Memory size36.1 KiB
2025-01-29T19:43:29.767588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length9.4340909
Min length1

Characters and Unicode

Total characters4151
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique438 ?
Unique (%)99.5%

Sample

1st row196,631,588
2nd row174,597,137
3rd row211,607,669
4th row136,569,078
5th row151,469,874
ValueCountFrequency (%)
2,190 2
 
0.5%
75,186,063 1
 
0.2%
25,936,342 1
 
0.2%
58,905,241 1
 
0.2%
196,631,588 1
 
0.2%
174,597,137 1
 
0.2%
211,607,669 1
 
0.2%
136,569,078 1
 
0.2%
151,469,874 1
 
0.2%
175,421,034 1
 
0.2%
Other values (429) 429
97.5%
2025-01-29T19:43:30.507959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 804
19.4%
1 409
9.9%
3 356
8.6%
4 340
8.2%
5 338
8.1%
2 333
8.0%
6 328
7.9%
7 322
7.8%
9 321
 
7.7%
8 319
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3347
80.6%
Other Punctuation 804
 
19.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 409
12.2%
3 356
10.6%
4 340
10.2%
5 338
10.1%
2 333
9.9%
6 328
9.8%
7 322
9.6%
9 321
9.6%
8 319
9.5%
0 281
8.4%
Other Punctuation
ValueCountFrequency (%)
, 804
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4151
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 804
19.4%
1 409
9.9%
3 356
8.6%
4 340
8.2%
5 338
8.1%
2 333
8.0%
6 328
7.9%
7 322
7.8%
9 321
 
7.7%
8 319
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4151
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 804
19.4%
1 409
9.9%
3 356
8.6%
4 340
8.2%
5 338
8.1%
2 333
8.0%
6 328
7.9%
7 322
7.8%
9 321
 
7.7%
8 319
 
7.7%

Spotify Popularity
Real number (ℝ)

Missing 

Distinct64
Distinct (%)16.9%
Missing4221
Missing (%)91.8%
Infinite0
Infinite (%)0.0%
Mean70.836412
Minimum1
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.1 KiB
2025-01-29T19:43:30.854864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17.9
Q170
median76
Q380
95-th percentile87
Maximum96
Range95
Interquartile range (IQR)10

Descriptive statistics

Standard deviation18.791663
Coefficient of variation (CV)0.26528254
Kurtosis4.8082823
Mean70.836412
Median Absolute Deviation (MAD)5
Skewness-2.2768989
Sum26847
Variance353.12661
MonotonicityNot monotonic
2025-01-29T19:43:31.321261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78 27
 
0.6%
80 26
 
0.6%
74 25
 
0.5%
77 23
 
0.5%
79 19
 
0.4%
73 17
 
0.4%
76 16
 
0.3%
68 14
 
0.3%
75 14
 
0.3%
72 13
 
0.3%
Other values (54) 185
 
4.0%
(Missing) 4221
91.8%
ValueCountFrequency (%)
1 4
0.1%
2 1
 
< 0.1%
3 2
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%
9 1
 
< 0.1%
12 3
0.1%
13 1
 
< 0.1%
15 1
 
< 0.1%
16 3
0.1%
ValueCountFrequency (%)
96 1
 
< 0.1%
95 1
 
< 0.1%
92 6
0.1%
91 1
 
< 0.1%
90 1
 
< 0.1%
89 1
 
< 0.1%
88 2
 
< 0.1%
87 10
0.2%
86 12
0.3%
85 9
0.2%

YouTube Views
Text

Missing 

Distinct413
Distinct (%)100.0%
Missing4187
Missing (%)91.0%
Memory size36.1 KiB
2025-01-29T19:43:31.937092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length11.118644
Min length6

Characters and Unicode

Total characters4592
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique413 ?
Unique (%)100.0%

Sample

1st row84,274,754
2nd row116,347,040
3rd row122,599,116
4th row1,096,100,899
5th row77,373,957
ValueCountFrequency (%)
240,970,670 1
 
0.2%
107,550,212 1
 
0.2%
35,724,356 1
 
0.2%
4,233,958 1
 
0.2%
1,256,973,582 1
 
0.2%
16,322,756,555 1
 
0.2%
156,882,014 1
 
0.2%
359,896,095 1
 
0.2%
265,180,930 1
 
0.2%
118,208,096 1
 
0.2%
Other values (403) 403
97.6%
2025-01-29T19:43:32.654325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 922
20.1%
1 467
10.2%
3 407
8.9%
2 393
8.6%
9 356
 
7.8%
8 354
 
7.7%
0 354
 
7.7%
4 346
 
7.5%
5 335
 
7.3%
6 335
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3670
79.9%
Other Punctuation 922
 
20.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 467
12.7%
3 407
11.1%
2 393
10.7%
9 356
9.7%
8 354
9.6%
0 354
9.6%
4 346
9.4%
5 335
9.1%
6 335
9.1%
7 323
8.8%
Other Punctuation
ValueCountFrequency (%)
, 922
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4592
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 922
20.1%
1 467
10.2%
3 407
8.9%
2 393
8.6%
9 356
 
7.8%
8 354
 
7.7%
0 354
 
7.7%
4 346
 
7.5%
5 335
 
7.3%
6 335
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 922
20.1%
1 467
10.2%
3 407
8.9%
2 393
8.6%
9 356
 
7.8%
8 354
 
7.7%
0 354
 
7.7%
4 346
 
7.5%
5 335
 
7.3%
6 335
 
7.3%

YouTube Likes
Text

Missing 

Distinct411
Distinct (%)100.0%
Missing4189
Missing (%)91.1%
Memory size36.1 KiB
2025-01-29T19:43:33.186801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length8.5815085
Min length3

Characters and Unicode

Total characters3527
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique411 ?
Unique (%)100.0%

Sample

1st row1,713,126
2nd row3,486,739
3rd row2,228,730
4th row10,629,796
5th row3,670,188
ValueCountFrequency (%)
569,377 1
 
0.2%
9,101,589 1
 
0.2%
2,749,668 1
 
0.2%
1,825,761 1
 
0.2%
437,980 1
 
0.2%
69,990 1
 
0.2%
14,661,425 1
 
0.2%
48,757,673 1
 
0.2%
3,080,503 1
 
0.2%
4,907,193 1
 
0.2%
Other values (401) 401
97.6%
2025-01-29T19:43:33.964726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 704
20.0%
1 360
10.2%
2 349
9.9%
3 294
8.3%
6 288
8.2%
9 271
 
7.7%
4 262
 
7.4%
5 254
 
7.2%
7 254
 
7.2%
0 246
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2823
80.0%
Other Punctuation 704
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 360
12.8%
2 349
12.4%
3 294
10.4%
6 288
10.2%
9 271
9.6%
4 262
9.3%
5 254
9.0%
7 254
9.0%
0 246
8.7%
8 245
8.7%
Other Punctuation
ValueCountFrequency (%)
, 704
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3527
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 704
20.0%
1 360
10.2%
2 349
9.9%
3 294
8.3%
6 288
8.2%
9 271
 
7.7%
4 262
 
7.4%
5 254
 
7.2%
7 254
 
7.2%
0 246
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3527
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 704
20.0%
1 360
10.2%
2 349
9.9%
3 294
8.3%
6 288
8.2%
9 271
 
7.7%
4 262
 
7.4%
5 254
 
7.2%
7 254
 
7.2%
0 246
 
7.0%

TikTok Posts
Text

Missing 

Distinct351
Distinct (%)99.7%
Missing4248
Missing (%)92.3%
Memory size36.1 KiB
2025-01-29T19:43:34.512296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length7.7755682
Min length1

Characters and Unicode

Total characters2737
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique350 ?
Unique (%)99.4%

Sample

1st row5,767,700
2nd row674,700
3rd row3,025,400
4th row7,189,811
5th row16,400
ValueCountFrequency (%)
1,800,000 2
 
0.6%
12 1
 
0.3%
1,064,662 1
 
0.3%
1,500,000 1
 
0.3%
472 1
 
0.3%
826,261 1
 
0.3%
205,583 1
 
0.3%
91,700 1
 
0.3%
61,144 1
 
0.3%
793,105 1
 
0.3%
Other values (341) 341
96.9%
2025-01-29T19:43:35.256115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 519
19.0%
0 388
14.2%
1 284
10.4%
3 235
8.6%
2 216
7.9%
4 213
7.8%
7 194
 
7.1%
6 182
 
6.6%
8 178
 
6.5%
9 171
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2218
81.0%
Other Punctuation 519
 
19.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 388
17.5%
1 284
12.8%
3 235
10.6%
2 216
9.7%
4 213
9.6%
7 194
8.7%
6 182
8.2%
8 178
8.0%
9 171
7.7%
5 157
7.1%
Other Punctuation
ValueCountFrequency (%)
, 519
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2737
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 519
19.0%
0 388
14.2%
1 284
10.4%
3 235
8.6%
2 216
7.9%
4 213
7.8%
7 194
 
7.1%
6 182
 
6.6%
8 178
 
6.5%
9 171
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2737
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 519
19.0%
0 388
14.2%
1 284
10.4%
3 235
8.6%
2 216
7.9%
4 213
7.8%
7 194
 
7.1%
6 182
 
6.6%
8 178
 
6.5%
9 171
 
6.2%

TikTok Likes
Text

Missing 

Distinct366
Distinct (%)100.0%
Missing4234
Missing (%)92.0%
Memory size36.1 KiB
2025-01-29T19:43:35.852633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length10.273224
Min length2

Characters and Unicode

Total characters3760
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique366 ?
Unique (%)100.0%

Sample

1st row651,565,900
2nd row35,223,547
3rd row275,154,237
4th row1,078,757,968
5th row214,943,489
ValueCountFrequency (%)
16,093,695 1
 
0.3%
29,584,940 1
 
0.3%
338,546,668 1
 
0.3%
121,574,500 1
 
0.3%
184,500 1
 
0.3%
45,889,000 1
 
0.3%
1,088 1
 
0.3%
521,725,116 1
 
0.3%
25,348,800 1
 
0.3%
419,319,161 1
 
0.3%
Other values (356) 356
97.3%
2025-01-29T19:43:36.630815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 722
19.2%
1 382
10.2%
4 319
8.5%
0 316
8.4%
3 312
8.3%
2 307
8.2%
6 305
8.1%
8 284
 
7.6%
9 276
 
7.3%
5 275
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3038
80.8%
Other Punctuation 722
 
19.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 382
12.6%
4 319
10.5%
0 316
10.4%
3 312
10.3%
2 307
10.1%
6 305
10.0%
8 284
9.3%
9 276
9.1%
5 275
9.1%
7 262
8.6%
Other Punctuation
ValueCountFrequency (%)
, 722
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3760
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 722
19.2%
1 382
10.2%
4 319
8.5%
0 316
8.4%
3 312
8.3%
2 307
8.2%
6 305
8.1%
8 284
 
7.6%
9 276
 
7.3%
5 275
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 722
19.2%
1 382
10.2%
4 319
8.5%
0 316
8.4%
3 312
8.3%
2 307
8.2%
6 305
8.1%
8 284
 
7.6%
9 276
 
7.3%
5 275
 
7.3%

TikTok Views
Text

Missing 

Distinct366
Distinct (%)100.0%
Missing4234
Missing (%)92.0%
Memory size36.1 KiB
2025-01-29T19:43:37.188303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length11.849727
Min length3

Characters and Unicode

Total characters4337
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique366 ?
Unique (%)100.0%

Sample

1st row5,332,281,936
2nd row208,339,025
3rd row3,369,120,610
4th row14,603,725,994
5th row2,938,686,633
ValueCountFrequency (%)
136,260,517 1
 
0.3%
534,915,313 1
 
0.3%
3,804,584,163 1
 
0.3%
974,656,200 1
 
0.3%
2,100,000 1
 
0.3%
360,017,000 1
 
0.3%
22,234 1
 
0.3%
7,499,234,052 1
 
0.3%
207,317,000 1
 
0.3%
5,456,156,211 1
 
0.3%
Other values (356) 356
97.3%
2025-01-29T19:43:37.843781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 918
21.2%
1 384
8.9%
0 378
8.7%
2 353
 
8.1%
8 349
 
8.0%
7 344
 
7.9%
3 341
 
7.9%
4 338
 
7.8%
5 328
 
7.6%
9 307
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3419
78.8%
Other Punctuation 918
 
21.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 384
11.2%
0 378
11.1%
2 353
10.3%
8 349
10.2%
7 344
10.1%
3 341
10.0%
4 338
9.9%
5 328
9.6%
9 307
9.0%
6 297
8.7%
Other Punctuation
ValueCountFrequency (%)
, 918
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4337
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 918
21.2%
1 384
8.9%
0 378
8.7%
2 353
 
8.1%
8 349
 
8.0%
7 344
 
7.9%
3 341
 
7.9%
4 338
 
7.8%
5 328
 
7.6%
9 307
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4337
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 918
21.2%
1 384
8.9%
0 378
8.7%
2 353
 
8.1%
8 349
 
8.0%
7 344
 
7.9%
3 341
 
7.9%
4 338
 
7.8%
5 328
 
7.6%
9 307
 
7.1%
Distinct370
Distinct (%)98.9%
Missing4226
Missing (%)91.9%
Memory size36.1 KiB
2025-01-29T19:43:38.443951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length11.15508
Min length2

Characters and Unicode

Total characters4172
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique367 ?
Unique (%)98.1%

Sample

1st row150,597,040
2nd row156,380,351
3rd row373,784,955
4th row3,351,188,582
5th row112,763,851
ValueCountFrequency (%)
1,318,392 3
 
0.8%
3,117,914 2
 
0.5%
934,654 2
 
0.5%
7,289,707,052 1
 
0.3%
1,063,591,802 1
 
0.3%
300,548,195 1
 
0.3%
426,461,749 1
 
0.3%
56,344,836 1
 
0.3%
5,822,984 1
 
0.3%
1,745,168,821 1
 
0.3%
Other values (360) 360
96.3%
2025-01-29T19:43:39.235552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 844
20.2%
1 410
9.8%
2 375
9.0%
4 365
8.7%
3 358
8.6%
5 341
8.2%
6 317
 
7.6%
7 310
 
7.4%
8 296
 
7.1%
0 282
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3328
79.8%
Other Punctuation 844
 
20.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 410
12.3%
2 375
11.3%
4 365
11.0%
3 358
10.8%
5 341
10.2%
6 317
9.5%
7 310
9.3%
8 296
8.9%
0 282
8.5%
9 274
8.2%
Other Punctuation
ValueCountFrequency (%)
, 844
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4172
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 844
20.2%
1 410
9.8%
2 375
9.0%
4 365
8.7%
3 358
8.6%
5 341
8.2%
6 317
 
7.6%
7 310
 
7.4%
8 296
 
7.1%
0 282
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4172
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 844
20.2%
1 410
9.8%
2 375
9.0%
4 365
8.7%
3 358
8.6%
5 341
8.2%
6 317
 
7.6%
7 310
 
7.4%
8 296
 
7.1%
0 282
 
6.8%

Apple Music Playlist Count
Real number (ℝ)

High correlation  Missing 

Distinct225
Distinct (%)57.3%
Missing4207
Missing (%)91.5%
Infinite0
Infinite (%)0.0%
Mean135.29262
Minimum1
Maximum859
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.1 KiB
2025-01-29T19:43:39.515684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q144
median102
Q3195
95-th percentile369.2
Maximum859
Range858
Interquartile range (IQR)151

Descriptive statistics

Standard deviation121.62005
Coefficient of variation (CV)0.89894077
Kurtosis3.8763658
Mean135.29262
Median Absolute Deviation (MAD)65
Skewness1.5923456
Sum53170
Variance14791.437
MonotonicityNot monotonic
2025-01-29T19:43:39.745967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 15
 
0.3%
67 7
 
0.2%
37 7
 
0.2%
56 6
 
0.1%
99 5
 
0.1%
2 4
 
0.1%
33 4
 
0.1%
94 4
 
0.1%
138 4
 
0.1%
11 4
 
0.1%
Other values (215) 333
 
7.2%
(Missing) 4207
91.5%
ValueCountFrequency (%)
1 15
0.3%
2 4
 
0.1%
3 2
 
< 0.1%
4 3
 
0.1%
5 1
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
8 2
 
< 0.1%
9 2
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
859 1
< 0.1%
581 1
< 0.1%
554 1
< 0.1%
549 1
< 0.1%
513 1
< 0.1%
507 1
< 0.1%
470 1
< 0.1%
465 1
< 0.1%
459 2
< 0.1%
455 1
< 0.1%

AirPlay Spins
Text

Missing 

Distinct388
Distinct (%)97.0%
Missing4200
Missing (%)91.3%
Memory size36.1 KiB
2025-01-29T19:43:40.304112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length5.7425
Min length1

Characters and Unicode

Total characters2297
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique380 ?
Unique (%)95.0%

Sample

1st row40,975
2nd row40,778
3rd row74,333
4th row1,474,799
5th row12,185
ValueCountFrequency (%)
1 5
 
1.2%
2 3
 
0.8%
29 2
 
0.5%
59 2
 
0.5%
734 2
 
0.5%
5 2
 
0.5%
1,309 2
 
0.5%
181 2
 
0.5%
115 1
 
0.2%
395,572 1
 
0.2%
Other values (378) 378
94.5%
2025-01-29T19:43:40.986322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 346
15.1%
1 279
12.1%
2 230
10.0%
4 206
9.0%
3 205
8.9%
5 191
8.3%
8 183
8.0%
6 180
7.8%
7 172
7.5%
9 165
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1951
84.9%
Other Punctuation 346
 
15.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 279
14.3%
2 230
11.8%
4 206
10.6%
3 205
10.5%
5 191
9.8%
8 183
9.4%
6 180
9.2%
7 172
8.8%
9 165
8.5%
0 140
7.2%
Other Punctuation
ValueCountFrequency (%)
, 346
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 346
15.1%
1 279
12.1%
2 230
10.0%
4 206
9.0%
3 205
8.9%
5 191
8.3%
8 183
8.0%
6 180
7.8%
7 172
7.5%
9 165
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 346
15.1%
1 279
12.1%
2 230
10.0%
4 206
9.0%
3 205
8.9%
5 191
8.3%
8 183
8.0%
6 180
7.8%
7 172
7.5%
9 165
7.2%

SiriusXM Spins
Text

Missing 

Distinct270
Distinct (%)82.8%
Missing4274
Missing (%)92.9%
Memory size36.1 KiB
2025-01-29T19:43:41.610765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.9601227
Min length1

Characters and Unicode

Total characters965
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique232 ?
Unique (%)71.2%

Sample

1st row684
2nd row3
3rd row536
4th row2,182
5th row1
ValueCountFrequency (%)
2 6
 
1.8%
1 5
 
1.5%
3 5
 
1.5%
6 5
 
1.5%
57 4
 
1.2%
13 3
 
0.9%
45 3
 
0.9%
117 3
 
0.9%
29 2
 
0.6%
14 2
 
0.6%
Other values (260) 288
88.3%
2025-01-29T19:43:42.550651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 157
16.3%
2 119
12.3%
3 106
11.0%
4 99
10.3%
6 80
8.3%
7 76
7.9%
5 74
7.7%
9 69
7.2%
8 68
7.0%
0 60
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 908
94.1%
Other Punctuation 57
 
5.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 157
17.3%
2 119
13.1%
3 106
11.7%
4 99
10.9%
6 80
8.8%
7 76
8.4%
5 74
8.1%
9 69
7.6%
8 68
7.5%
0 60
 
6.6%
Other Punctuation
ValueCountFrequency (%)
, 57
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 965
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 157
16.3%
2 119
12.3%
3 106
11.0%
4 99
10.3%
6 80
8.3%
7 76
7.9%
5 74
7.7%
9 69
7.2%
8 68
7.0%
0 60
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 965
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 157
16.3%
2 119
12.3%
3 106
11.0%
4 99
10.3%
6 80
8.3%
7 76
7.9%
5 74
7.7%
9 69
7.2%
8 68
7.0%
0 60
 
6.2%

Deezer Playlist Count
Real number (ℝ)

High correlation  Missing 

Distinct169
Distinct (%)44.5%
Missing4220
Missing (%)91.7%
Infinite0
Infinite (%)0.0%
Mean84.534211
Minimum1
Maximum632
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.1 KiB
2025-01-29T19:43:42.747397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q122
median51
Q399.5
95-th percentile308.15
Maximum632
Range631
Interquartile range (IQR)77.5

Descriptive statistics

Standard deviation106.4887
Coefficient of variation (CV)1.2597113
Kurtosis8.2371006
Mean84.534211
Median Absolute Deviation (MAD)35
Skewness2.6950075
Sum32123
Variance11339.843
MonotonicityNot monotonic
2025-01-29T19:43:42.963798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 9
 
0.2%
5 9
 
0.2%
7 9
 
0.2%
4 8
 
0.2%
41 8
 
0.2%
25 7
 
0.2%
65 6
 
0.1%
42 6
 
0.1%
43 6
 
0.1%
51 6
 
0.1%
Other values (159) 306
 
6.7%
(Missing) 4220
91.7%
ValueCountFrequency (%)
1 9
0.2%
2 5
0.1%
3 4
0.1%
4 8
0.2%
5 9
0.2%
6 5
0.1%
7 9
0.2%
8 5
0.1%
9 4
0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
632 1
< 0.1%
584 1
< 0.1%
570 1
< 0.1%
564 1
< 0.1%
557 1
< 0.1%
541 1
< 0.1%
512 1
< 0.1%
502 1
< 0.1%
482 1
< 0.1%
445 1
< 0.1%

Deezer Playlist Reach
Text

Missing 

Distinct380
Distinct (%)100.0%
Missing4220
Missing (%)91.7%
Memory size36.1 KiB
2025-01-29T19:43:43.598171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length8.2763158
Min length1

Characters and Unicode

Total characters3145
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique380 ?
Unique (%)100.0%

Sample

1st row17,598,718
2nd row10,422,430
3rd row36,321,847
4th row24,684,248
5th row17,660,624
ValueCountFrequency (%)
13,718,632 1
 
0.3%
41,414,565 1
 
0.3%
40,725,482 1
 
0.3%
6 1
 
0.3%
5,783,693 1
 
0.3%
52,179 1
 
0.3%
22,062,193 1
 
0.3%
347,997 1
 
0.3%
19,469,488 1
 
0.3%
16,295,286 1
 
0.3%
Other values (370) 370
97.4%
2025-01-29T19:43:44.347125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 619
19.7%
1 310
9.9%
3 288
9.2%
2 273
8.7%
4 267
8.5%
7 247
 
7.9%
5 241
 
7.7%
8 238
 
7.6%
6 229
 
7.3%
9 227
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2526
80.3%
Other Punctuation 619
 
19.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 310
12.3%
3 288
11.4%
2 273
10.8%
4 267
10.6%
7 247
9.8%
5 241
9.5%
8 238
9.4%
6 229
9.1%
9 227
9.0%
0 206
8.2%
Other Punctuation
ValueCountFrequency (%)
, 619
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3145
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 619
19.7%
1 310
9.9%
3 288
9.2%
2 273
8.7%
4 267
8.5%
7 247
 
7.9%
5 241
 
7.7%
8 238
 
7.6%
6 229
 
7.3%
9 227
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3145
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 619
19.7%
1 310
9.9%
3 288
9.2%
2 273
8.7%
4 267
8.5%
7 247
 
7.9%
5 241
 
7.7%
8 238
 
7.6%
6 229
 
7.3%
9 227
 
7.2%

Amazon Playlist Count
Real number (ℝ)

High correlation  Missing 

Distinct139
Distinct (%)36.7%
Missing4221
Missing (%)91.8%
Infinite0
Infinite (%)0.0%
Mean58.889182
Minimum1
Maximum210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.1 KiB
2025-01-29T19:43:44.516199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.9
Q126.5
median50
Q385
95-th percentile137.5
Maximum210
Range209
Interquartile range (IQR)58.5

Descriptive statistics

Standard deviation42.2416
Coefficient of variation (CV)0.71730662
Kurtosis0.3781085
Mean58.889182
Median Absolute Deviation (MAD)28
Skewness0.86772733
Sum22319
Variance1784.3528
MonotonicityNot monotonic
2025-01-29T19:43:44.693769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 9
 
0.2%
38 7
 
0.2%
15 7
 
0.2%
45 6
 
0.1%
54 6
 
0.1%
39 6
 
0.1%
44 6
 
0.1%
33 6
 
0.1%
40 6
 
0.1%
10 6
 
0.1%
Other values (129) 314
 
6.8%
(Missing) 4221
91.8%
ValueCountFrequency (%)
1 9
0.2%
2 4
0.1%
3 2
 
< 0.1%
4 4
0.1%
5 3
 
0.1%
6 3
 
0.1%
7 2
 
< 0.1%
8 3
 
0.1%
9 5
0.1%
10 6
0.1%
ValueCountFrequency (%)
210 1
< 0.1%
189 1
< 0.1%
188 1
< 0.1%
184 1
< 0.1%
177 1
< 0.1%
174 1
< 0.1%
172 1
< 0.1%
168 1
< 0.1%
167 1
< 0.1%
163 1
< 0.1%

Pandora Streams
Text

Missing 

Distinct371
Distinct (%)100.0%
Missing4229
Missing (%)91.9%
Memory size36.1 KiB
2025-01-29T19:43:45.189117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length9.6469003
Min length2

Characters and Unicode

Total characters3579
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique371 ?
Unique (%)100.0%

Sample

1st row18,004,655
2nd row7,780,028
3rd row5,022,621
4th row190,260,277
5th row4,493,884
ValueCountFrequency (%)
1,005,626 1
 
0.3%
23,155,471 1
 
0.3%
1,354,692 1
 
0.3%
26,252,264 1
 
0.3%
12,171,026 1
 
0.3%
9,961,769 1
 
0.3%
44,850,379 1
 
0.3%
283,089 1
 
0.3%
132,624,772 1
 
0.3%
70,291,476 1
 
0.3%
Other values (361) 361
97.3%
2025-01-29T19:43:45.779494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 691
19.3%
1 371
10.4%
2 320
8.9%
3 305
8.5%
4 289
8.1%
5 284
7.9%
6 275
 
7.7%
8 268
 
7.5%
0 268
 
7.5%
7 257
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2888
80.7%
Other Punctuation 691
 
19.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 371
12.8%
2 320
11.1%
3 305
10.6%
4 289
10.0%
5 284
9.8%
6 275
9.5%
8 268
9.3%
0 268
9.3%
7 257
8.9%
9 251
8.7%
Other Punctuation
ValueCountFrequency (%)
, 691
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3579
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 691
19.3%
1 371
10.4%
2 320
8.9%
3 305
8.5%
4 289
8.1%
5 284
7.9%
6 275
 
7.7%
8 268
 
7.5%
0 268
 
7.5%
7 257
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3579
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 691
19.3%
1 371
10.4%
2 320
8.9%
3 305
8.5%
4 289
8.1%
5 284
7.9%
6 275
 
7.7%
8 268
 
7.5%
0 268
 
7.5%
7 257
 
7.2%
Distinct362
Distinct (%)99.5%
Missing4236
Missing (%)92.1%
Memory size36.1 KiB
2025-01-29T19:43:46.354931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length5.6648352
Min length1

Characters and Unicode

Total characters2062
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique360 ?
Unique (%)98.9%

Sample

1st row22,931
2nd row28,444
3rd row5,639
4th row203,384
5th row7,006
ValueCountFrequency (%)
100 2
 
0.5%
3,216 2
 
0.5%
5,639 1
 
0.3%
203,384 1
 
0.3%
7,006 1
 
0.3%
50,982 1
 
0.3%
57,372 1
 
0.3%
5,762 1
 
0.3%
842 1
 
0.3%
21,172 1
 
0.3%
Other values (352) 352
96.7%
2025-01-29T19:43:47.104708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 338
16.4%
1 255
12.4%
2 219
10.6%
3 190
9.2%
6 164
8.0%
9 162
7.9%
4 159
7.7%
5 155
7.5%
8 145
7.0%
7 139
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1724
83.6%
Other Punctuation 338
 
16.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 255
14.8%
2 219
12.7%
3 190
11.0%
6 164
9.5%
9 162
9.4%
4 159
9.2%
5 155
9.0%
8 145
8.4%
7 139
8.1%
0 136
7.9%
Other Punctuation
ValueCountFrequency (%)
, 338
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2062
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 338
16.4%
1 255
12.4%
2 219
10.6%
3 190
9.2%
6 164
8.0%
9 162
7.9%
4 159
7.7%
5 155
7.5%
8 145
7.0%
7 139
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2062
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 338
16.4%
1 255
12.4%
2 219
10.6%
3 190
9.2%
6 164
8.0%
9 162
7.9%
4 159
7.7%
5 155
7.5%
8 145
7.0%
7 139
6.7%

Soundcloud Streams
Text

Missing 

Distinct199
Distinct (%)100.0%
Missing4401
Missing (%)95.7%
Memory size36.1 KiB
2025-01-29T19:43:47.558195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length8.8341709
Min length3

Characters and Unicode

Total characters1758
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique199 ?
Unique (%)100.0%

Sample

1st row4,818,457
2nd row6,623,075
3rd row7,208,651
4th row207,179
5th row9,438,601
ValueCountFrequency (%)
4,818,457 1
 
0.5%
6,623,075 1
 
0.5%
7,208,651 1
 
0.5%
207,179 1
 
0.5%
9,438,601 1
 
0.5%
3,679,709 1
 
0.5%
1,594,605 1
 
0.5%
1,313,357 1
 
0.5%
12,038,034 1
 
0.5%
871,978 1
 
0.5%
Other values (189) 189
95.0%
2025-01-29T19:43:48.339680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 347
19.7%
1 200
11.4%
5 160
9.1%
9 143
8.1%
2 142
8.1%
4 141
8.0%
6 131
 
7.5%
8 126
 
7.2%
3 126
 
7.2%
7 123
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1411
80.3%
Other Punctuation 347
 
19.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 200
14.2%
5 160
11.3%
9 143
10.1%
2 142
10.1%
4 141
10.0%
6 131
9.3%
8 126
8.9%
3 126
8.9%
7 123
8.7%
0 119
8.4%
Other Punctuation
ValueCountFrequency (%)
, 347
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1758
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 347
19.7%
1 200
11.4%
5 160
9.1%
9 143
8.1%
2 142
8.1%
4 141
8.0%
6 131
 
7.5%
8 126
 
7.2%
3 126
 
7.2%
7 123
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 347
19.7%
1 200
11.4%
5 160
9.1%
9 143
8.1%
2 142
8.1%
4 141
8.0%
6 131
 
7.5%
8 126
 
7.2%
3 126
 
7.2%
7 123
 
7.0%

Shazam Counts
Text

Missing 

Distinct389
Distinct (%)100.0%
Missing4211
Missing (%)91.5%
Memory size36.1 KiB
2025-01-29T19:43:48.888972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length8.4832905
Min length2

Characters and Unicode

Total characters3300
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique389 ?
Unique (%)100.0%

Sample

1st row2,669,262
2nd row1,118,279
3rd row5,285,340
4th row11,822,942
5th row457,017
ValueCountFrequency (%)
2,682,219 1
 
0.3%
85,957 1
 
0.3%
3,288 1
 
0.3%
4,613,459 1
 
0.3%
2,669,262 1
 
0.3%
28,653,448 1
 
0.3%
3,273,690 1
 
0.3%
857,885 1
 
0.3%
3,146,346 1
 
0.3%
986,887 1
 
0.3%
Other values (379) 379
97.4%
2025-01-29T19:43:49.606235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 671
20.3%
1 326
9.9%
2 286
8.7%
3 268
 
8.1%
6 266
 
8.1%
8 258
 
7.8%
7 256
 
7.8%
4 253
 
7.7%
9 247
 
7.5%
5 240
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2629
79.7%
Other Punctuation 671
 
20.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 326
12.4%
2 286
10.9%
3 268
10.2%
6 266
10.1%
8 258
9.8%
7 256
9.7%
4 253
9.6%
9 247
9.4%
5 240
9.1%
0 229
8.7%
Other Punctuation
ValueCountFrequency (%)
, 671
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 671
20.3%
1 326
9.9%
2 286
8.7%
3 268
 
8.1%
6 266
 
8.1%
8 258
 
7.8%
7 256
 
7.8%
4 253
 
7.7%
9 247
 
7.5%
5 240
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 671
20.3%
1 326
9.9%
2 286
8.7%
3 268
 
8.1%
6 266
 
8.1%
8 258
 
7.8%
7 256
 
7.8%
4 253
 
7.7%
9 247
 
7.5%
5 240
 
7.3%

TIDAL Popularity
Unsupported

Missing  Rejected  Unsupported 

Missing4600
Missing (%)100.0%
Memory size36.1 KiB

Explicit Track
Categorical

Missing 

Distinct2
Distinct (%)0.4%
Missing4155
Missing (%)90.3%
Memory size36.1 KiB
0.0
255 
1.0
190 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1335
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 255
 
5.5%
1.0 190
 
4.1%
(Missing) 4155
90.3%

Length

2025-01-29T19:43:49.786704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-29T19:43:49.925535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 255
57.3%
1.0 190
42.7%

Most occurring characters

ValueCountFrequency (%)
0 700
52.4%
. 445
33.3%
1 190
 
14.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 890
66.7%
Other Punctuation 445
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 700
78.7%
1 190
 
21.3%
Other Punctuation
ValueCountFrequency (%)
. 445
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1335
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 700
52.4%
. 445
33.3%
1 190
 
14.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1335
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 700
52.4%
. 445
33.3%
1 190
 
14.2%

Interactions

2025-01-29T19:43:17.226935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-29T19:43:12.977720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-29T19:43:13.894156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-29T19:43:14.806375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-29T19:43:15.660546image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-29T19:43:16.445385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-29T19:43:17.340448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-29T19:43:13.115042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-29T19:43:14.034866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-29T19:43:14.964107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-29T19:43:15.782575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-29T19:43:16.554384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-29T19:43:17.473463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-29T19:43:13.250079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-29T19:43:14.246631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-29T19:43:15.104946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-29T19:43:15.903576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-29T19:43:16.712153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-29T19:43:17.590449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-29T19:43:13.373167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-29T19:43:14.386918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-29T19:43:15.229250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-29T19:43:16.027795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-29T19:43:16.869317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-29T19:43:17.711463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-29T19:43:13.503517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-29T19:43:14.518897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-29T19:43:15.372065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-29T19:43:16.171903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-29T19:43:16.988941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-29T19:43:17.830449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-29T19:43:13.741526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-29T19:43:14.664807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-29T19:43:15.522158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-29T19:43:16.328384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-29T19:43:17.101935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-01-29T19:43:50.021236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
All Time RankAmazon Playlist CountApple Music Playlist CountDeezer Playlist CountExplicit TrackSpotify PopularityTrack Score
All Time Rank1.000-0.435-0.248-0.2670.077-0.263-1.000
Amazon Playlist Count-0.4351.0000.6390.6600.1940.3770.435
Apple Music Playlist Count-0.2480.6391.0000.8310.0500.1810.248
Deezer Playlist Count-0.2670.6600.8311.0000.1590.2290.267
Explicit Track0.0770.1940.0500.1591.0000.1650.016
Spotify Popularity-0.2630.3770.1810.2290.1651.0000.263
Track Score-1.0000.4350.2480.2670.0160.2631.000

Missing values

2025-01-29T19:43:18.045451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-29T19:43:18.532914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-01-29T19:43:19.305060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

TrackAlbum NameArtistRelease DateISRCAll Time RankTrack ScoreSpotify StreamsSpotify Playlist CountSpotify Playlist ReachSpotify PopularityYouTube ViewsYouTube LikesTikTok PostsTikTok LikesTikTok ViewsYouTube Playlist ReachApple Music Playlist CountAirPlay SpinsSiriusXM SpinsDeezer Playlist CountDeezer Playlist ReachAmazon Playlist CountPandora StreamsPandora Track StationsSoundcloud StreamsShazam CountsTIDAL PopularityExplicit Track
0MILLION DOLLAR BABYMillion Dollar Baby - SingleTommy Richman4/26/2024QM24S24025281.0725.4390,470,93630,716196,631,58892.084,274,7541,713,1265,767,700651,565,9005,332,281,936150,597,040210.040,97568462.017,598,718114.018,004,65522,9314,818,4572,669,262NaN0.0
1Not Like UsNot Like UsKendrick Lamar5/4/2024USUG124009102.0545.9323,703,88428,113174,597,13792.0116,347,0403,486,739674,70035,223,547208,339,025156,380,351188.040,778367.010,422,430111.07,780,02828,4446,623,0751,118,279NaN1.0
2i like the way you kiss meI like the way you kiss meArtemas3/19/2024QZJ8424003873.0538.4601,309,28354,331211,607,66992.0122,599,1162,228,7303,025,400275,154,2373,369,120,610373,784,955190.074,333536136.036,321,847172.05,022,6215,6397,208,6515,285,340NaN0.0
3FlowersFlowers - SingleMiley Cyrus1/12/2023USSM122097774.0444.92,031,280,633269,802136,569,07885.01,096,100,89910,629,7967,189,8111,078,757,96814,603,725,9943,351,188,582394.01,474,7992,182264.024,684,248210.0190,260,277203,384NaN11,822,942NaN0.0
4HoudiniHoudiniEminem5/31/2024USUG124033985.0423.3107,034,9227,223151,469,87488.077,373,9573,670,18816,400NaNNaN112,763,851182.012,185182.017,660,624105.04,493,8847,006207,179457,017NaN1.0
5Lovin On MeLovin On MeJack Harlow11/10/2023USAT223113716.0410.1670,665,438105,892175,421,03483.0131,148,0911,392,5934,202,367214,943,4892,938,686,6332,867,222,632138.0522,0424,65486.017,167,254152.0138,529,36250,9829,438,6014,517,131NaN1.0
6Beautiful ThingsBeautiful ThingsBenson Boone1/18/2024USWB123070167.0407.2900,158,75173,118201,585,71486.0308,723,1454,120,760NaN29,584,940534,915,3134,601,579,812280.0383,478429168.048,197,850154.065,447,47657,372NaN9,990,302NaN0.0
7Gata OnlyGata OnlyFloyyMenor2/2/2024QZL3824060498.0375.8675,079,15340,094211,236,94092.0228,382,5681,439,4953,500,000338,546,6683,804,584,1632,112,581,620160.017,2213087.033,245,59553.03,372,4285,762NaN6,063,523NaN1.0
8Danza Kuduro - Cover��������������������� - ������������������ -MUSIC LAB JPN6/9/2024TCJPA24637089.0355.71,653,018,119115NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.0
9BAND4BAND (feat. Lil Baby)BAND4BAND (feat. Lil Baby)Central Cee5/23/2024USSM1240435410.0330.690,676,57310,400184,199,41986.032,735,244988,682325,800121,574,500974,656,200174,706,874191.03,82311778.010,800,09892.01,005,6268423,679,709666,302NaN1.0
TrackAlbum NameArtistRelease DateISRCAll Time RankTrack ScoreSpotify StreamsSpotify Playlist CountSpotify Playlist ReachSpotify PopularityYouTube ViewsYouTube LikesTikTok PostsTikTok LikesTikTok ViewsYouTube Playlist ReachApple Music Playlist CountAirPlay SpinsSiriusXM SpinsDeezer Playlist CountDeezer Playlist ReachAmazon Playlist CountPandora StreamsPandora Track StationsSoundcloud StreamsShazam CountsTIDAL PopularityExplicit Track
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